Our bias discovery approach works in two stages.
First, we use BK to discover functional dependencies and relational properties, such as irreflexivity, asymmetry, and antitransitivity.
To do so, we use a bottom-up approach [1]} implemented in answer set programming (ASP).
Second, we use the properties to build constraints to restrict the hypothesis space.
For instance, if we discover that even and odd are mutually exclusive, we build constraints to prohibit rules with both the body literals odd(A) and even(A).
We use the constraints to bootstrap a constraint-driven ILP system [2]}.  The constraints remove non-optimal hypotheses from the hypothesis space so that the system never considers them when searching for a solution.
